Tag: data science

Best Industries for Data Scientists

These days with the kind of momentum that the industry of data science is growing at, it has become quite the golden time to pursue a career as a Data Scientist. It has become quite the pan-industry career, spanning various different sectors and creating multiple opportunities for professionals to derive insights through data.

Did you know that technology sector alone compromises of about 41% of the data scientists in the sector? Whereas 13% of the data scientists are seen working in the marketing sector, then 11% function in the corporate world and 9% perform consultative duties. Apart from these mainstream options, there are also data scientists working in the health care and pharmaceutical sectors, they measure up to 7%.

Now that we have fairly established what a hot and happening career data science is, let us talk about some of the best career options where a data scientist can flourish. Now it is important to keep in mind the highly valuable nature of data science and the various things that it can actually result into. Which is why we will be breaking away from the run of the mill, ordinary kind of career and shift to a little more interesting bundle of the same.

The first career in our list is the industry of Biotechnology, surprised aren’t you? But you shouldn’t really be, mainly because since the beginning of time, the field of science and medicine have been interdependent on each other. History is witness to the fact that as technology improved, almost all of the nations shifted their focus to growth and development of public health facilities.

The reason why this field will require data scientists because, as a global community we are almost on the brink of decoding the human genome and unlocking all the secrets that have remained hidden so far. For this very purpose, we would need data the size of a mountain, because it happens to be an unbelievable feat to achieve.

Energy is one other industry which is on its path to vocalize its demands for data scientists. As time passes, new potential resources of energy are developed. And in order to harness the energy output of these modern resources. Various new campaigns like the storage of crude oil, exploration of new ways to extract energy and mineral wealth from the earth and brain storming of new ways of transporting the energy sources and finding new ways to exploit solar energy and clean energy. All of these need expert data scientists to help them along the way.

Another industry which will be in need of data scientists is the quality control and source inspection industry, then the transportation industry might also mirror demands of the same. Telecommunications is one very dynamic industry that will be in need of data scientists in the near future. In order to feed this demand, droves of people have decided to take up this as their career option. Some choose the path of gainful experience during employment whereas some choose the vocational training path, with comprehensive courses offered by training institutes like Imarticus Learning.

With the growing application of the field of data science in various verticals across the industry, there is no doubt in the fact that any stone would be left unturned. This is very true especially in the context of India’s strategic policy of security. This comes right after the recent news that CISF which is also known as the Central Industrial Security Force, which happens to be a national agency has announced that there is a need to secure all of the important assets especially when it comes to evidence, which is why they have come up with a novel idea of creating a ‘media lab’ as well as a social media monitoring room at its base near Chennai.

Now what is interesting is the way this is going to be achieved. Precisely through the use of social media directly. There are numerous social media trends that will be followed and tracked and then the data analytics professionals with analyse them and process the same in order to ensure as well as monitor the security of our country’s most vital assets both physical in nature like the airports, the nuclear reactors and aerospace installations as well as virtual in nature like confidential data stored in the cloud.

This media lab is supposed to be created basically for the purpose of usage by the Pattern Research for Institutional Social Media (PRISM) analytics at the Chennai base of CISF. This whole project will include a team of trained professionals who are basically CISF agents. These officers would be trained in order to learn how to track social media trends, the various news and snippets available online and offline, reports and various other indicators across various platforms in order to collate them and help dish them out as pieces of vital information. These would then be referred to as pieces of “actionable intelligence” to various places of transit like airports and so on.

The paramilitary forces will also be roped in this exercise and there will be various platforms that will function as their sources. These platforms would range from websites like Twitter, Facebook, YouTube, and Flickr and so on in order to keep a check on any such activity that guarantees some sort of suspicion. The Director General of CISF, Mr. OP Singh said, “WE are doing this on an experimental basis. The PRISM control room is based in the southern part of the country as we have a sizable number of units that have armed security cover in that part. Based on the experiences this smart centre will be further bolstered. There will be a special team of our men who will be keeping a track of social media trends in order to keep all of their physical assets safe and sound from any injury.”

The success of this experiment would ensure that the security of our country will get a huge boost and thus at the same students will want to train more in the field of data analytics through institutes like Imarticus Learning.

The sheer amount of data that is being generated today can totally be called as the marketers’ dream. Anyone in the marketing industry might know the very importance of being able to work with an audience which is not just the target audience of the firm, but the right kind of target audience. This is all come to be true due to the presence of data science and big data. This data is usually generated in huge amounts through various Internet of Things devices that are used by the majority population in their homes, social media profiles and so on.

Did you know that today with the right kind of tools, you could actually go ahead and drive your own campaign on the basis of the right kind of data and by gathering the same from the right kind of places as well? It is almost like the business which deal in data science, have found out a huge gold mine for themselves by landing on such great amounts of right kind of big data. This way they are able to have successful conversions of their very own customers and help in gaining profits. But on the other hand not all data scientists or business owners are able to actually reap the benefits of the same.

Here are a few tips to help you along in your journey to transform your business.

Focus on finding the right kind of customers

Data science, more often than not goes along a long way in trying to create the face of your entire business campaign. Always focus on the best kind of advertising methods which will help you land in the perfect kind of position where you can not only attract the most amount of attention but also ensure that there is the right kind of business optimization taking place. For instance let’s talk about banner ads, if no one happens to be clicking on them, why not show them the door?

Landing on your target audience

The purpose of data science is not only to show you how many people clicked on your advertisement, rather it is to show you a lot, lot more in general. The number of clicks are really instrumental, but at the same time what is more important is the fact that who exactly are the people who are doing this clicking thing. Data Science does just that for you, it gives you a whole picture of who exactly is doing the clicking and how exactly the other factors affect the same.

The Need To Find The Best Candidate

While we did begin with the marketing side of things, which is not really all that data can do. This field is not just meant to entice people but to do a lot, lot more. Which is why it is important to find out the right kind of candidate who can help you find out and also take advantage of the same. Finding professionals trained for the industry like the ones churned out by professional training institutes like Imarticus Learning is a great way to start your journey.

Companies, especially in the field of data science, are known to be quite the picky employers, for the very reason that there exists a lot of competition in the industry. There has been a study quite recently, the findings of which state that a number of data scientists who have been already working as professionals, learned to work the way they do not through a proper course of formal training but rather through either a professional training course or through those courses that are available online for free.

Professional training courses today, have been very much on the rise mainly because the industry has begun to actually be transformed into this quest for the most suited employee. This is because the companies today don’t just want anyone who can wing it, but rather want a professional who is able to sort out things and get the work done in the most effective manner possible. Which is why candidates are today looking to get thoroughly trained in order to become industry endorsed and are similarly looking to do professional training courses to achieve the same. In the context of India, there are quite a few professional training institutes like Imarticus Learning which are rising in popularity in terms of training candidates to be industry endorsed.

When companies look for candidates, there are few chosen tips that they all follow

Firstly the companies start off with the basics and begin their search for individuals and candidates who would be proficient in computer science or have a degree in a related field. According to statistics, there are about 30% of such candidates, who are currently working as data science professionals and had been computer science undergraduates before entering the field.

There are so many jobs available and so much demand that statistics state that professionals are always on the lookout for jobs, almost two hours a week sometimes. This shows that although a part of the industry there are many candidates who are willing to move on to better positions and better career roles in data science.

Companies are always looking out for candidates and while doing so they ensure that they find out the right kind of candidate with the right kind of educational qualifications. While a data scientist or a data analyst would be their cup of tea, but someone who is a software engineer or a software developer would have fewer chances of getting employed by that very company.

The most important trait, however, is the skillset. Many companies are extremely stringent about the same which is why candidates looking for employment have to up their game quite seriously. This is also the reason why the competition when it comes to choosing candidates also increases. Statistics state that only 4% of the employed data scientists can say quite sure that they are equipped to perform the roles expected out of them as they have the required skillset to do so.

Just like there happen to be various categories of statisticians like for instance biostatistician, econometricians, and operations research specialists and so on, there are also categories of data scientists. In any particular industry, when it comes to one discipline, there still happen to be multiple people acting and working in similar yet different fashion. A simpler example would be the various categories of business analysts. These could range from professionals who have an expertise in the field of marketing, or product or ever finance. Similar is the case with the field of Data Science. Did you know that many data scientists, don’t even happen to have the same title that of a data scientist?

Now let us begin with the various categories of data scientists that are here, thriving and functioning in the industry. Firstly there are those who have statistics as their strong suit. They happen to be involved in development of new and improved statistical theories for the development of data science. They have a proper expertise in various subjects such as statistical modelling, experimental design, and sampling, clustering, data reduction and so on.

Then there are those data scientists who have maths as their strongest suit possible. These people are usually the ones employed in high tech governmental and non-governmental agencies like the national security agency, the operations and research departments and sometimes they are even astronomers or people working for the military and defence services of a particular country. These are the creamiest of the creamy layer of the society and are usually found to be collecting, analysing and extracting value out of data.

Those professionals who are supposed to be having expertise in the field of data engineering are known to be fitting right in into the third category of data scientists. These professionals generally work with a number of data analytical tools like Hadoop as well as work with database or file system optimization or the newest hot word in the market, are involved in the process of data plumbing.

The next few categories are although not as big hits as the three that precede them, but at the same time are extremely important as well. For instance, those who are involved in the field of machine learning, happen to be extremely strong with algorithms and computational complexity, which happen to be their areas of work influence.

Those professionals who work in the business category are usually those who work in optimization as a part of the various decision sciences. Their functions were apparently carried out by various business analysts earlier in the big gun type of companies. Whereas those who usually work with code development are found to be working in the field of software engineering. Then there are those data scientists who fall in the category of data visualization, spatial data and many more.

For a data scientist or a data aspirant to actually fall in any of the three categories, it is very important for them to first develop their faculties. For which most of these aspirants usually join professional training institutes like Imarticus Learning.

Why Must a Professional Learn Python?

Data Analytical languages or as they are popularly known, programming languages tend to be a little on the difficult side when it comes to learning them. Of all of them, it is believed that Python is one such language or tool, which is pretty easy to learn, especially when we compare it to the others. The syntax that this programming environment provides is not really that ceremonial and is quite easy to get a hang of. This helps all of those non-programmers work really efficiently in this software. When it comes to learning python or teaching it to someone, it is easier to do so with examples as opposed to teaching say Ruby or Perl mainly because of the lesser number of rules and special cases that Python has.

Many might have heard this name ‘Python’ for the very first time in the past couple of years. But what is interesting to know that this programming language has existed in the industry for the past 27 years, which is a lot more time. What then makes this tool so relevant in spite of being so old? It is the fact that Python can be pretty much applied to any and every software development or operations scenario that you can find in the world today. You can make use of python if you are looking to manage local and cloud infrastructure, or developing websites or have to work with SQL or even if you are looking for a custom function in order to make do with Pig or Hive, then Python applies there as well, this is a major reason as to why professionals especially those working in the analytical fields must learn python.

With python it is so easy that once you learn the language, you can very easily leverage the platform. It happens to be backed by PyPi which is pronounced as Pie Pie. Herein a user can make use of more than 85,000 modules as well as scripts. These modules are formulated in such a way that they are able to deliver pre-packaged functionality to any of the local python environments as well as solve a number of problems like the working of databases and the glitches therein, implementation of computer vision, and execution of advanced data analytics such as sentiment analysis or building of RESTful web services.

These days it has become quite a norm that any job you happen to be looking for, you will most probably be in need of having a skillset that is defined by big data and analytics which is why it becomes quite important for one to thoroughly understand the working of Python. As this data analytical tool happens to be a strong presence in the various areas of coding as well as data analytics it is sure to rule the roost in the near future. This is why we see a lot of professionals opting to learn Python from various professional training institutes like Imarticus Learning.

So you’ve chosen to move past canned calculations and begin to code your own machine learning techniques. Perhaps you have a thought for a cool better approach for grouping information, or possibly you are disappointed by the confinements in your most loved measurable characterization bundle.

In either case, the better your insight into information structures and calculations, the less demanding time you’ll have when it comes time to code up.

The data structures utilized as a part of machine learning are fundamentally not quite the same as those utilized as a part of different regions of programming advancement. Due to the size and trouble of a considerable lot of the issues, be that as it may, having a truly strong handle on the nuts and bolts is basic.

Likewise, in light of the fact that machine learning is an exceptionally numerical field, one should remember how information structures can be utilized to take care of scientific issues and how they are numerical questions in their own privilege.

There are two approaches to characterize information structures: by their usage and by their operation.

By usage, the stray pieces of how they are modified and the genuine stockpiling designs. What they look like outwardly is less essential than what’s happening in the engine. For information structures classed by operation or dynamic information sorts, it is the inverse — their outside appearance and operation is more vital than how they are actualized, and truth be told, they can for the most part be executed utilizing various diverse inner portrayals.

Along these lines, the most well-known sorts will be of the one-and two-dimensional assortment, relating to vectors and frameworks separately, however you will periodically experience three-or four-dimensional exhibits either for higher positioned tensors or to assemble cases of the previous.

While doing framework number-crunching, you should look over a bewildering assortment of libraries, information sorts, and even dialects. Numerous logical programming dialects, for example, Matlab, Interactive Data Language (IDL), and Python with the Numpy augmentation are outlined principally to work with vectors and lattices.

Connected List

A connected rundown comprises of a few independently allotted hubs. Every hub contains an information esteem in addition to a pointer to the following hub in the rundown. Additions, at steady time, are extremely proficient, however getting to an esteem is moderate and frequently requires looking over a significant part of the rundown.

Connected records are anything but difficult to join together and split separated. There are numerous varieties — for example, additions should be possible at either the head or the tail; the rundown can be doubly-connected and there are numerous comparable information structures in view of a similar rule, for example, the parallel tree underneath:

Double Tree

A double tree is like a connected rundown with the exception of that every hub has two pointers to consequent hubs rather than only one. The incentive in the left tyke is constantly not as much as the incentive in the parent hub, which thusly is littler than that of the correct tyke. In this manner, information in paired trees are consequently arranged. Both inclusion and get to are productive at O(log n) all things considered. Like connected records, they are anything but difficult to change into clusters and this is the reason for a tree-sort.

Stack

A stack is another progressive, requested information structure like a tree aside from rather than a flat requesting, it has a vertical requesting. This requesting applies along the chain of command, yet not crosswise over it: the parent is constantly bigger than the two its youngsters, however a hub of higher rank is not really bigger than a lower one that is not specifically underneath it.

Imarticus Learning is an esteemed institute which offers a number of industry endorsed courses in both finance and analytics.